activity
20192025
most citedGenerative Adversarial Networks (GAN) for compact beam source modelling in Monte Carlo simulations

34 citations · 41 across the 6 of their papers we have counts for

collaborators

6 papers

physics.med-ph2025

Five Years of Clinical Application of independent Monte Carlo-Based Patient-Specific Quality Assurance at the Maastro Proton Therapy Center

Ilaria Rinaldi, Giorgio Cartechini, Angelo Schiavi +6

At the Maastro Proton Therapy Center in Maastricht, patient-specific quality assurance (PSQA) using an independent GPU-accelerated Monte Carlo (MC) calculation has fully replaced c…

physics.med-ph2025★ 2 cited

GATE 10 Monte Carlo particle transport simulation -- Part I: development and new features

David Sarrut, Nicolas Arbor, Thomas Baudier +30

We present GATE version 10, a major evolution of the open-source Monte Carlo simulation application for medical physics, built on Geant4. This release marks a transformative evolut…

physics.med-ph2025★ 1 cited

GATE 10 Monte Carlo particle transport simulation -- Part II: architecture and innovations

Nils Krah, Nicolas Arbor, Thomas Baudier +29

Over the past years, we have developed GATE version 10, a major re-implementation of the long-standing Geant4-based Monte Carlo application for particle and radiation transport sim…

physics.med-ph2021

Relative stopping power precision in time-of-flight proton CT

Nils Krah, Denis Dauvergne, Jean Michel Létang +2

Proton computed tomography (CT) is similar to x-ray CT but relies on protons rather than photons to form an image. In its most common operation mode, the measured quantity is the a…

physics.med-ph2019★ 4 cited

Investigations on physical and biological range uncertainties in Krakow proton beam therapy centre

Antoni Rucinski, Jakub Baran, Giuseppe Battistoni +15

Physical and biological range uncertainties limit the clinical potential of Proton Beam Therapy (PBT). In this proceedings, we report on two research projects, which we are conduct…

physics.med-ph2019★ 34 cited

Generative Adversarial Networks (GAN) for compact beam source modelling in Monte Carlo simulations

David Sarrut, Nils Krah, Jean-Michel Létang

A method is proposed and evaluated to model large and inconvenient phase space files used in Monte Carlo simulations by a compact Generative Adversarial Network (GAN). The GAN is t…